Personalized breast screening and follow-up in community clinics
Advancing Risk-based Breast Cancer Screening and Surveillance in Community Practice
This program uses personal risk factors, imaging, and AI to tailor mammogram schedules and extra scans for people who get breast screening.
Quick facts
| Grant type | P01 program project |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of California at Davis NIH-funded |
| Lab location | 1 site (Davis, United States) |
| Project ID | NIH-11182580 on NIH RePORTER |
What this research studies
From a patient's perspective, the program builds new risk models that combine your health history, mammogram features, and AI-based image information to better predict advanced breast cancer. The team will compare tailored screening intervals and the use of supplemental MRI for people judged to be higher risk. They will also study how patient, neighborhood, and clinic factors affect screening quality and explore whether targeted AI and other interventions can improve results in community practices. A separate project focuses on finding breast cancer survivors who are at higher risk of a second cancer and improving how they are watched over time.
Who could benefit from this research
Good fit: Ideal candidates are people eligible for routine breast cancer screening—including average-risk and higher-risk individuals—and breast cancer survivors receiving follow-up at participating clinics.
Not a fit: People who are not eligible for routine breast imaging (for example, those with bilateral mastectomies and no remaining breast tissue) or who cannot access participating clinics are unlikely to benefit directly.
Why it matters
Potential benefit: If successful, this work could lead to more accurate, personalized screening that finds dangerous cancers earlier while reducing unnecessary tests for low-risk people.
How similar studies have performed: Previous research shows risk models and AI can improve detection and risk stratification, but combining imaging, AI, and multilevel interventions across community settings is relatively new.
Where this research is happening
Davis, United States
- University of California at Davis — Davis, United States (Active)
Researchers
- Principal investigator: Miglioretti, Diana L — University of California at Davis
- Study coordinator: Miglioretti, Diana L
About this research
- This is an active NIH-funded research project — typically early-stage science, not a clinical trial accepting patient enrollment.
- Some NIH-funded labs run parallel clinical studies or seek volunteers for related work. To check, contact the principal investigator or institution listed above.
- For full project details, budget, and progress reports, visit the official NIH RePORTER page below.